SERS-Integrated Microneedles: Bridging Nanoplasmonics and Microsampling for Advanced Bioanalysis
Bibliographic record
Abstract
Sensitive analytical techniques capable of in situ measurements in biological tissues with high selectivity and rapid response are essential for health monitoring, disease diagnosis, agriculture management, and food safety. However, conventional biological sampling is often invasive, expensive, and inconvenient. Microneedle (MN) technology offers a noninvasive, quick, and self-administered approach for in vivo sampling of extracellular fluids that are rich in biomarkers and metabolites indicative of health status. By integrating MNs with highly sensitive surface-enhanced Raman spectroscopy (SERS), the hybrid technique provides unprecedented convenience, user compliance, and analytical sensitivity for biomonitoring. The versatility of SERS-integrated MNs (SERS-MNs), along with their integration into portable, self-administered devices, makes them ideal for point-of-care testing. SERS-MNs can also be incorporated into wearable medical devices for real-time, long-term biochemical monitoring with high temporal resolution. This perspective explores the emerging applications of SERS-MNs by critically examining the key requirements in materials, structural design, and fabrication methods, while elucidating their underlying working principles. We further assess current challenges and highlight future opportunities, providing insights to advance their use in clinical diagnostics, precision agriculture, and food safety. This work offers a systematic discussion on the integration of SERS-MNs into wearable devices for long-term, real-time health monitoring, opening new possibilities to empower individuals in proactive health management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".